Wave energy resource assessment in the Mediterranean, the Italian perspective

Renewable Energy 50 (2013) 938e949

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Technical note

Wave energy resource assessment in the Mediterranean, the Italian perspective Luca Liberti a, *, Adriana Carillo b, Gianmaria Sannino b a b

Institute for Environmental Protection and Research ISPRA, via Vitaliano Brancati 48, 00144 Rome, Italy ENEA - Ocean Modelling Unit, via Anguillarese 301, 00123 Rome, Italy

a r t i c l e i n f o

a b s t r a c t

Article history: Received 26 February 2012 Accepted 5 August 2012 Available online 18 September 2012

In this paper we present a high resolution assessment of the wave energy resources in the Mediterranean. The energy resources are evaluated through of a numerical simulation performed on the entire Mediterranean basin for the period 2001e2010 using a third generation ocean wave model. The model results are extensively validated against most of the available wave buoy and satellite altimeter data. Starting from the model results a detailed analysis of wave energy availability in the Mediterranean Sea is carried out. The western Sardinia coast and the Sicily Channel are found to be among the most productive areas in the whole Mediterranean. Simulation results show the presence of significant spatial variations of wave power availability even on relatively small spatial scales along these two coastlines. For a number of selected locations in these two areas we present an in-depth investigation of the distribution of wave energy among wave heights, periods and directions. Seasonal and inter-annual variability of wave energy potential are also analyzed and discussed. Ó 2012 Elsevier Ltd. All rights reserved.

Keywords: Wave energy Mediterranean Italy Numerical wave model Wave energy resources Wave energy atlas

1. Introduction Wave energy appears to be one of the most promising among the renewable resources. It is estimated that it will undergo a significant growth in the next decades as soon as the Wave Energy Converters (WECs) technology becomes more mature [1]. Currently a number of different WECs have been proposed and tested [2] but large scale commercial installations are not yet in operation. The WECs technology is believed to be at a crucial stage of its development cycle when well-established solutions emerge from the realm of research. Research on wave energy production is particularly advanced in countries bordering large oceans where the greatest wave energy potential is found. In Europe most of the pilot plants either planned or in operation are located along the Atlantic coast in countries such as Ireland, Portugal, Spain, Norway and the UK [3]. Energy availability is certainly a major factor affecting wave energy production but high energy potential usually implies exceptional wave conditions during extreme events. Such conditions pose serious engineering challenges to the design and deployment of WECs increasing the costs of development, production, installation, maintenance and insurance of these devices. On the other hand, in calmer and semi-enclosed seas such

* Corresponding author. Tel.: þ39 0650074559. E-mail addresses: [email protected] (L. Liberti), [email protected] (A. Carillo), [email protected] (G. Sannino). 0960-1481/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.renene.2012.08.023

as the Mediterranean, where lower amounts of wave energy are available, many technical issues related to extreme sea climate could be more easily solved, possibly making wave energy production still economically viable. From this point of view, wave energy production in the Mediterranean is especially appealing for countries like Italy having relatively long coastlines. Feasibility studies of wave energy plants require a detailed knowledge of energy occurrence, of its temporal and spatial variability and of its distribution among different sea states. At present, an extensive and accurate estimation of wave energy for the Italian seas is not yet available. Wave energy atlases rely on wave measurement obtained from buoys, satellite and output from model hindcasts. In recent years, several authors presented global wave energy atlases, see for instance [4e6] or [7]. These works either don’t include results for the Mediterranean or are based on models with a spatial resolution too coarse to capture smaller scale spatial variation in wave energy availability which are important to identify suitable locations for wave energy production in relatively small basins like the Mediterranean. Wave energy atlases of the Italian coast were recently developed using wave parameters measured by buoys located off the coast [8,9]. Wave buoys provide the most accurate and direct measure of waves parameters. However, time series obtained from wave buoys describe wave climate only locally and often present large data gaps caused by temporary failure of the buoy or by routine maintenance operations. Wave height and period do not generally show steep spatial gradients in the open ocean but substantial spatial variations are observed in enclosed

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Fig. 1. Model computational domain and bathymetry.

seas where land obstructions deeply influence wave generation and propagation [10]. In these regions wave models represent the most important tool to assess wave energy distribution. One of the most extensive study of the wave climate in the Mediterranean sea was carried out in the framework of the WW-MEDATLAS project [11]. In this project, climatology maps of wave height, period and direction were produced using 900 points extracted from a 0.5 resolution WAM operational model run at the European Center for MediumRange Weather Forecast (ECMWF). The authors presented a methodology to correct the underestimation of wave heights found in the ECMWF operational model by calibrating the model output with corrective factors obtained from satellite and buoy measurements [12]. The MEDATLAS project represent one of the first large scale attempts to provide wave climate analysis for the Mediterranean region. Another notable initiative is EU HIPOCAS project which produced a 44 years hindcast of the wave climate in the Mediterranean at a maximum resolution of 0.1 forced by a 0.5 resolution atmospheric model [13] [14]. More recently, a 20 years hindcast study of the Mediterranean Sea was carried out using a higher resolution atmospheric model and results of wave model simulation at 0.1 resolution were presented [15]. All these works were not explicitly devoted to wave energy potential evaluation and do not directly provide wave energy data. Currently a high resolution study of wave energy distribution in the Mediterranean appears to be lacking. Filling this gap is the main purpose of the this paper where we describe a wave energy atlas of the Mediterranean Sea obtained by running for the 10 years period 2001e2010 a 1/16 resolution wave model forced by the wind fields provided by the ECMWF. The results of the model were analyzed to define the average wave energy availability and to address the problem of its temporal variability both in terms of seasonal cycle and interannual fluctuations as suggested by some authors (see for instance [16] or [4]). This paper is organized as follows: model setup and validation is described in Section 2, wave energy potential in the Mediterranean and, more in detail, around the Italian coast, is analyzed and discussed in Section 3, the conclusions of our study are outlined in Section 4. 2. Model description and validation 2.1. Model set-up Wave simulations were performed using a parallel version of the WAM wave model Cycle 4.5.3 [17]. The model domain covers the entire Mediterranean Sea, from 5.50 W to 36.125 E of longitude

and from 30.2 N to 45.825 N of latitude. The domain was discretized with a regular grid of 667  251 nodes in spherical coordinates with a uniform resolution of 1/16 in each direction, corresponding to a linear mesh size of 5e7 km. By extending the computational domain over the entire Mediterranean, we were able to describe the wave climate along the Italian coast at relatively high spatial resolution taking into account both local wave generation and propagation from distant areas, avoiding errors introduced by nesting procedures. Model bathymetry was calculated from the General Bathymetric Chart of the Oceans (GEBCO) 30 arc-second gridded data set [18] by averaging the depths of data points falling in each computational cell. Cells were classified as land if the data point nearest to the cell centroid was found to lay onshore. This automatic procedure generally provided a good approximation of the sea-land boundaries requiring only minimal manual adjustments. The directional wave energy density spectrum function was discretized using 36 directional bins and 32 frequency bins starting from 0.06 Hz with relative size increments of 0.1 between one frequency bin and the next. The model was forced with six-hourly wind fields obtained from ECMWF operational analysis at 1/4 spatial resolution [19]. Further details on the ECMWF data set can be found in [20] and [21]. The effects of currents and variations in sea surface elevation were not taken into account in the model. Fig. 1 shows computational domain and model bathymetry. The Mediterranean is considered as a closed basin neglecting any wave propagation from neighboring seas. Swell propagation from the Atlantic might not be negligible in the proximity of the Gibraltar Strait, however a detailed description of the wave energy in this area is beyond the scope of the present work. Wave climate simulations were performed for the period 2001e2010 extracting the integral wave parameters significant wave height (Hs), mean wave period (Tm), significant wave period (Te) and mean direction (qm) from the entire model domain every 3 h. Model results were validated against satellite and buoy measurements. Table 1 Characteristics of satellites used for model Hs validation. Satellite

Repeat cycle (days)

Used period

Track separation at equator (km)

Topex-Poseidon Jason-1 Jason-2 Envisat ERS-2

10 10 10 35 35

Jan. 2001eOct. 2005 Jan. 2002eDec. 2010 Jun. 2008eDec. 2010 Oct. 2002eOct. 2010 Jan. 2001eDec. 2006 Jan. 2008eDec. 2010

315 315 315 80 80

940

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Fig. 2. Ground tracks of satellites considered in model validation. Thick gray lines identify Jason-1 and Jason-2 tracks. Black lines partially overlying the gray ones symbolize TopexPoseidon tracks while thin dashed lines represent Envisat and ERS-2 tracks.

2.2. Validation against satellite data Satellite radar altimeters provide Hs measurements in areas far from the coast where wave buoys are not normally located. Hs measures from satellite were used to evaluate the overall behavior of the model over the entire Mediterranean Sea. Table 1 lists the satellite delivering altimeter measurements that were in operation during the simulated period while Fig. 2 shows the satellite tracks over the Mediterranean over which measures are periodically performed. Satellite data was downloaded from the AVISO web site [22] and processed to remove outliers according the method described by Queffeulou and Bentamy [23]. This procedure is based on the statistical analysis of the differences between consecutive data points along the tracks. Following this preliminary step, Hs values were matched to model values by extracting the model output from the grid cell enclosing the satellite point at the nearest available time. No temporal or spatial interpolation of model or satellite data was performed. Table 2 reports for each satellite the values of indices generally used to evaluate model performance. We included in our analysis the root mean square error (rmse), the bias between model and measures (bias), the scatter index (si) and the slope of the best fit line passing through the origin (slope). Given a series of n model values yi and corresponding measures xi the indices are calculated as follows:

bias ¼

n 1X ðyi  xi Þ; n

(1)

Pn xy slope ¼ Pin¼ 1 i i : i ¼ 1 xi xi

(4)

We also considered the Willmott index of agreement (d) [24] which is defined as:

" d ¼ 1

n X i¼1

# n   X  0 2 0    : ðyi  xi Þ = y i  xi 2

(5)

i¼1

where y0i ¼ yi  x, x0i ¼ xi  x and x is the average of the observed values. The d index is bounded in the interval [0,1]. A value of 1 implies perfect match between model and observations. Values in the table show good agreement between model and satellite Hs with small biases not exceeding 0.15 m, best fit lines with slopes above 0.9 and d values above 0.92. A similar analysis is presented in a recent paper [25] where the performances of various 0.1 resolution wave models in the Mediterranean Sea were evaluated, over a two months period, by forcing them with wind fields with various resolutions ranging from 10 to 40 km. The results of the study, in terms of bias, rmse, slope and si are similar to the ones we found. For instance, the best fit line slopes of the comparison between model and satellites Jason and ERS-2 data is reported to lie in range 0.7e0.99; the root mean square error obtained by comparing satellite data with the WAM model forced with 10 km ALADIN winds is reported to be around 0.55 with bias of about 0.3. Fig. 3

i¼1

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n u 1 X rmse ¼ t ðy  xi Þ2 ; n  1 i¼1 i

500

10

(3)

250

8

100 6

50

4

Table 2 Statistics of satellite and model significant wave height (Hs) comparison for the entire Mediterranean. Satellite

Samples

Bias (m)

Rmse (m)

Slope

si

d

Topex-Poseidon Jason-1 Jason-2 Envisat ERS-2

457,000 910,133 242,766 695,768 363,336

0.128 0.028 0.024 0.141 0.011

0.331 0.362 0.366 0.385 0.426

0.912 0.979 1.018 0.921 0.962

0.279 0.304 0.303 0.310 0.400

0.957 0.951 0.950 0.943 0.929

Entries

rmse ; n 1X yi n i¼1

Hs - Model (m)

si ¼

12

(2)

10 5

2

0 0 0

2

4

6 8 Hs - Satellite (m)

10

12

Fig. 3. Scatter plot of model vs. Jason-1 Hs for the entire Mediterranean. Value pairs are grouped in 0.25 m wide bins, corresponding areas are painted according to the number of entries in each bin. Dashed line is the best fit line between model and satellite data points.

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Table 3 Statistics of satellite and model significant wave height (Hs) comparison for the Adriatic Sea. Satellite

Samples

Bias (m)

Rmse (m)

Slope

si

d

Topex-Poseidon Jason-1 Jason-2 Envisat ERS-2

21,889 9911 9447 32,832 24,043

0.256 0.108 0.107 0.165 0.289

0.407 0.436 0.404 0.476 0.475

0.763 0.897 0.906 0.751 0.753

0.438 0.440 0.417 0.595 0.485

0.891 0.895 0.896 0.845 0.815

shows the scatter plot of model and Jason-1 Hs over the entire Mediterranean. Considering the number of sample points analyzed, and the fact that the lighter gray tiles represent bins containing less than 5 samples, the data points dispersion shown in the plot is acceptable. Similar results are found for all the other satellites. As reported in [23] satellite measures of Hs tend to be less reliable for values lower than 0.5 m a fact that might explain the higher dispersion of the points in lower left section of the scatter plot. 2.3. Validation against buoy data

Fig. 4. Locations of RON wave buoys. Filled symbols indicate buoys included in model validation. Dashed grid lines are 200 km apart.

by qualitatively comparing the model and buoy time series. After assessing the good quality of the results, more rigorous comparisons were carried out by constructing scatter plots of corresponding model and buoy values and by computing the statistics given by Equations (1)e(5). We excluded from the analysis buoy records where the peak spectral period Tp fell in the infra-gravity waves range, above 20 s. Fig. 5 shows the Hs scatter plot for the Alghero buoy while Table 5 lists the values of the bias, si, slope, rmse and d for each buoy. By inspecting the values in Table 5 we observe that we can group the buoys in three subsets depending on the level of agreement between model and buoy values. The results obtained at Alghero, Crotone, Cetraro and Capo Gallo buoys are in very good agreement with the data with best fit line slopes around the unity, biases of the order of centimeters and scatter indices below 0.4. For a second set of buoys including La Spezia, Ponza and Cetraro the results appear to be still satisfactory but biases tend to be in the order of 0.1 m and best fit line slopes are below 0.9. Hs appears to be 8

500

Table 4 Data availability of RON wave buoys. Buoys indicated in bold were used for model validation.

Alghero Catania Crotone La Spezia Mazara del Vallo Monopoli Ortona Ponza Cetraro Ancona Capo Comino Capo Gallo Capo Linaro Punta della Maestra Cagliari

01/07/1989 01/07/1989 01/07/1989 01/07/1989 01/07/1989 01/07/1989 01/07/1989 01/07/1989 28/02/1999 10/03/1999 01/01/2004 01/01/2004 02/01/2004 02/01/2004 06/02/2007

05/04/2008 05/10/2006 15/07/2007 31/03/2007 04/04/2008 05/04/2008 24/03/2008 31/03/2008 05/04/2008 31/05/2006 12/09/2005 31/03/2008 12/09/2006 24/11/2004 02/03/2008

15,283 12,549 14,962 10,952 15,323 15,641 12,786 14,479 16,630 10,212 3813 9001 5441 2616 1986

21,210 16,827 19,093 18,240 21,207 21,209 21,113 21,169 21,209 15,812 5664 12,408 7872 7872 3120

72.1 80.2 78.4 60.0 72.3 73.7 60.6 68.4 78.4 64.6 67.3 72.5 69.1 62.8 63.7

250 100

(m)

First record Last record 3 hr Expected Valid records 3 hr records (%) after records 1/1/2001

Hs - Model

Buoy

6

50

4

Entries

Model results were also compared to buoy wave measurements collected by the Italian Wave measuring Network (Rete Ondametrica Nazionale, RON) managed by Institute for Environmental Protection and Research (ISPRA). Eight directional deep water buoys are in operation since July 1989 recording significant wave height Hs, peak period Tp, mean period Tm and mean wave direction qm. Until 2002 wave parameters were normally recorded every 3 h. During storms, when Hs exceeded a buoy-specific threshold, the recording rate was increased to 300 , in 2002 the measurement rate was set to 300 regardless of the sea state [26]. Wave measurements are currently available for each buoy starting from its initial deployment date up to mid 2008. In order to obtain a set of statistically homogeneous samples we took into account only three-hourly recorded data so that the measurements dating back to 2001 and 2002 could be included in our analysis. This choice is also justified by the 6-h temporal resolution of forcing wind fields which is significantly lower than the 300 buoy measuring rate. Buoys whose data record we judged too short were not used in the analysis. Table 4 gives an overview of the RON buoys data availability while Fig. 4 shows the location of the RON buoys. Buoy measures were compared to model output extracted from the nearest computational node. A first validation was performed

20 10 2 5 0 0 0

2

4 Hs - Buoy

6

8

(m)

Fig. 5. Correlation between buoy and model Hs at Alghero. Dashed line is the best fit line between model and buoy data points.

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Table 5 Statistics of buoy and model significant wave height (Hs) comparison. Buoy

Bias (m)

Rmse (m)

Slope

si

d

Alghero Ancona Catania Crotone La Spezia Mazara del Vallo Ortona Ponza Monopoli Cetraro Capo Gallo

0.005 0.214 0.178 0.004 0.143 0.013 0.150 0.103 0.124 0.070 0.019

0.311 0.361 0.308 0.276 0.283 0.257 0.284 0.273 0.307 0.241 0.255

0.985 0.725 0.747 0.993 0.851 1.022 0.753 0.892 0.836 0.897 1.040

0.278 0.477 0.501 0.374 0.354 0.253 0.460 0.328 0.427 0.341 0.339

0.974 0.889 0.876 0.949 0.951 0.971 0.911 0.953 0.917 0.960 0.962

0.6

Frequency

Buoy

0.4

0.2

Table 6 Statistics of buoy and model wave average spectral period Tm comparison.

0.0 0

Bias (s)

Rmse (s)

Slope

si

d

Alghero Ancona Catania Crotone La Spezia Mazara del Vallo Ortona Ponza Monopoli Cetraro Capo Gallo

0.230 0.349 0.142 0.206 0.506 0.195 0.063 0.035 0.327 0.156 0.307

0.791 0.959 1.163 0.805 1.049 0.780 0.718 0.646 1.017 1.009 0.925

1.032 0.883 0.996 1.031 1.107 1.030 0.992 0.993 0.877 1.004 1.048

0.172 0.240 0.274 0.207 0.257 0.177 0.198 0.160 0.257 0.223 0.225

0.915 0.718 0.725 0.847 0.813 0.865 0.790 0.881 0.656 0.818 0.822

generally underestimated for the buoys located in the Adriatic Sea. This is probably due to the rather coarse resolution of the wind fields compared to the extent of the Adriatic Sea. As argued by [27], the effect of the complex orography, which is not well described at the resolution of atmospheric model, plays in this case an important role in defining the wind fields. In fact, the comparison of satellite altimeter data in the Adriatic Sea in Table 3 shows better agreement between model and measures when compared to the buoys. Satellite tracks are located further offshore than the wave buoys where the influence of the land is less important. A more accurate description of the wave energy potential in the Adriatic Sea requires wind fields at higher temporal and spatial resolution, however, preliminary and conservative estimates of the wave energy potential can still be obtained by the present model. Hs is underestimated also at the Catania buoy. This buoy is located in a sheltered position characterized by a mild wave climate, two factors that are known to contribute in reducing the model performance. We further validated the wave model by comparing buoy and model average spectral period Tm. The average wave spectral period Tm is used here as an estimate of wave spectra frequency Table 7 Circular statistics of buoy and model wave average spectral direction qm comparison. Buoy

Bias (Deg.)

var

Alghero Ancona Catania Crotone La Spezia Mazara del Vallo Ortona Ponza Monopoli Cetraro Capo Gallo

4.51 9.41 14.81 8.09 2.94 11.00 13.48 8.76 5.00 6.39 5.21

0.036 0.230 0.053 0.085 0.056 0.057 0.101 0.116 0.121 0.063 0.026

100

200 Average Wave Direction

300

(Deg.)

Fig. 6. Frequency distribution of model and buoy average wave direction at Alghero. Only records with Hs > 1 m are considered. Incoming wave direction expressed in degrees clockwise from the north.

distribution since the wave power flux, as it will be shown in the next section, is calculated using the significant wave period Te which is not readily available from the buoy measures. As shown in Table 6, the regression line slope is almost one for each buoy with the exception of Monopoli and Ancona where the model underestimates Tm and the bias is negative. For all the remaining buoys the model tends to overestimate the wave average period; where the regression line slope exceeds the unity, this trend is more marked at higher values of the period. The overestimation measured by the bias is relatively small. Bias values exceeding 0.5 s are found only at La Spezia. Rmse values are considerably higher than the ones obtained for Hs and d values are lower implying that model Tm values have higher dispersion. In any case, the absolute difference between buoy and model average period is less than 1 s in more than 70% of samples for all the buoys with the only exception of Catania. As a final validation step, following the approach used by Hanson [28], we compared buoys and model mean wave directions. This analysis was carried out by calculating the directional bias and the directional variance of the difference between model and buoy

Buoy 0.6

Frequency

Buoy

Model

Model

0.4

0.2

0.0 0

100

200 Average Wave Direction

300

(Deg.)

Fig. 7. Same as Fig. 6 for Mazara del Vallo buoy.

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Fig. 8. Distribution of average power per unit crest in the Mediterranean between 2001 and 2010. Wave power values are tabulated for selected sites marked by circles in Table 8.

wave directions as described by Mardia and Jupp [29]. Given a set of n model directions yi and corresponding measures xi the directional bias (bias ) and directional variance (var ) of the residuals yixi are calculated as:

S ¼

n 1X sinðyi  xi Þ; n i¼1

n 1X C ¼ cosðyi  xi Þ; n

significant period. Te represents the period of a sinusoidal wave having the same energy content of the sea state [31]. When sea state is described by the directional wave energy density spectrum function S(f,q), Te has the expression:

Z2p ZN

(6) Te ¼ (7)

m1 ¼ m0

i¼1

R ¼



2

C þS

12

;

  bias ¼ arctan S=C ;   var ¼ 1  R :

(8) (9)

Z2p ZN

(12)

Sðf ; qÞdf dq

0

Equation (12) is equivalent to the definition of the integral wave parameter Te calculated by the WAM code that was used in our calculations [17]. 3.1. Wave power distribution in the Mediterranean

3. Model results and discussion The results of the model were used to prepare wave energy maps of the entire Mediterranean and to analyze the wave energy availability and variability along the Italian coast. Following [30], in deep water, the available energy flux per unit crest can be expressed as:

rg 2 Te Hs2 64p

:

(10)

Table 7 shows for each buoy the values of the computed statistics. Since the wave direction is not well defined for calmer sea states, we excluded from the analysis all the records where the buoy Hs was less than 1 m. The agreement between the model and buoy direction seems satisfactory for the scope of the present study. Circular biases between 10 and 15 are found only at Catania, Ortona and Mazara del Vallo buoys and the values of the circular variance of the residuals are on the lower end of the admissible range [0,1] with the highest value found at Ancona. The main directional characteristics of the wave climate are well described by the model as shown in Figs. 6 and 7 where the model and buoy frequency distribution of average wave direction are compared.

J ¼

0

0

0 2

f 1 Sðf ; qÞdf dq

(11)

where J is the energy flux in Watt per meter of wave crest, g is the gravity acceleration, r the sea water density assumed to be r ¼ 1025 kg/m3, Hs the significant wave height and Te the wave

Fig. 8 shows a map of the available wave power flux per unit crest averaged over the entire 10 years simulation period in the Mediterranean. The most productive area, showing average values

Table 8 Average wave power flux Jmean and average annual energy flux Emean over the entire simulation period at selected sites along the coast of the Mediterranean as shown in Fig. 8. Values were extracted from the second cell offshore. Site Location

Lon.

Lat.

Eannual Depth Jmean (kW/m) (MWh/m) (m)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

0 330 4500 4 150 000 3 260 1500 6 110 1500 10 110 1500 8 300 000 14 70 3000 17 180 4500 20 150 000 23 330 4500 24 450 000 30 260 1500 32 110 1500 34 480 4500 27 520 3000 23 330 4500 15 110 1500 9 410 1500 6 260 1500 0 220 3000

37 340 3000 40 40 3000 42 190 3000 42 530 1500 43 300 4500 41 570 000 40 300 4500 39 00 4500 38 120 000 35 420 000 38 490 3000 36 80 1500 34 530 1500 32 450 4500 31 190 3000 35 420 000 32 300 4500 37 270 000 37 120 000 36 00 4500

121 65 439 1476 83 786 782 615 1512 374 269 444 1290 252 420 374 161 250 2354 1428

Cabo de Palos (Es) Menorca (Es) Cabo Creus (Es) Hyères (Fr) Livorno (It) Ajaccio (Fr) Napoli (It) Crotone (It) Kefallonia (Gr) Ag. Gramvousa (Gr) Skyros (Gr) Gelydonia Burnu (Tr) Peyia (Cy) Haifa (Il) Ras El-Kanayis (Eg) Ras Al Hilal (Ly) Misrata (Ly) Ras Angela (Tn) Cap Bougaouni (Dz) Orano (Dz)

3.91 10.90 5.34 6.47 3.24 8.44 3.51 3.70 4.91 7.10 5.16 2.26 3.83 4.02 5.30 6.59 5.68 9.25 10.33 5.15

34.25 95.48 46.78 56.68 26.02 73.93 30.70 32.41 43.01 62.20 45.20 19.80 33.55 35.22 46.43 57.73 49.76 81.03 90.49 45.11

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Balearic Islands and north Africa. The neighboring region located in the Sicily channel, off the north-western and southern Sicilian coasts, is also very productive with an average wave energy flux per unit crest that reaches 9 kW/m. Slightly lower wave power appears to be available in central Mediterranean and southern Ionian Sea with average values not exceeding 8 kW/m near the coast in the eastern section. The western part of the Levantine basin exhibits similar values reaching almost 8 kW/m. The Adriatic Sea, where average wave power does not exceed 3 kW/m, is one of the least productive regions. Similar values are observed in the most sheltered parts of the Ionian and Tyrrhenian between the mainland and Corsica and near the Messina Strait. The Aegean Sea, despite its semi-enclosed configuration, appears to be more productive than the Adriatic Sea at least off the continental coast. The wave power actually available at locations near the coast follows the same spatial pattern but, as expected, amounts to lower values compared to the open sea. Wave energy availability near the coast for selected sites in the Mediterranean shown in Fig. 8 is summarized in Table 8. 3.2. Wave power distribution along the Italian coast

Fig. 9. Distribution of average wave power flux per unit crest on western Sardinia coastline. Values are calculated on a line located 12 km off the coast. Dashed grid lines are 50 km apart. Wave energy distribution among sea states at marked locations is described in Fig. 11.

above 12 kW/m, is located in the western Mediterranean between the Balearic Islands and the western coast of Sardinia. Wave power in this area is most easily accessible from the coasts of Sardinia,

A more detailed overview of the wave power distribution at locations sufficiently near the most productive areas of the Italian coast, western Sardinia, western and southern Sicily is presented in Figs. 9 and 10 where the average wave power per unit crest for the entire simulated period is calculated along a line placed approximately 12 km offshore. The 12 km distance from the coastline was selected because it corresponds to positions sufficiently near the coast to represent the wave climate right before the bottom topography starts to affect the wave field. The line lies, on average, on the second model cell from the land at depths always above 50 m. Wave energy in shallower areas, closer to the coast, tends to be affected by the influence of the bathymetry on wave propagation, which is not adequately taken into account at the spatial resolution of our model. Along western Sardinia the average power ranges from 7.5 to almost 12 kW/m. The highest values are observed on the northern and southern sections of the coast, between Alghero and Asinara

Fig. 10. Same as 9 but on western north-western and southern Sicily coastline. Wave energy distribution among sea states at marked locations is described in Fig. 12.

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Island and near San Pietro Island; along the intermediate section of the coast lower values between 8.5 and 10 kW/m are found. Around the northern and southern limit of the western Sardinia coastline average wave power drops sharply as soon as the exposure to the waves coming from W and NW declines. The north-western and southern coasts of Sicily have a lower potential with average wave power ranging between 2.5 and 6.5 kW/m. On the northern coast west of Palermo average wave power flux is between 4 and 5 kW/m gradually increasing to values between 5 and 6 kW/m between San Vito Lo Capo and Trapani. The most productive area is located along the coastal stretch that lies north of Mazara del Vallo where average power is above 6 kW/m reaching values around 7 kW/m near Favignana Island. The rest of the southern coast is the least productive with average power flux below 4.5 and as low as 2.5 kW/m. The only exception is the area between Punta Secca and Capo Passero where values are almost everywhere near 5 kW/m. Similarly to what was previously observed on the Sardinia coast there is a sharp decline in average power east of Palermo and north of Capo Passero. Figs. 9 and 10 show that average wave power exhibits a non-negligible spatial variability even at spatial scales of the order of 20 km. For instance, the average wave power just a few kilometers south of Alghero decreases almost 20%. Similar spatial variations can be observed around San Pietro Island in Sardinia and near Mazara del Vallo, Favignana Island and Punta Secca in Sicily. Such spatial variability cannot be adequately described by local buoy measurements or by models with lower spatial resolution. The average power is a useful parameter to identify promising areas for wave energy production, however, its values arise from the contribution of individual sea states distributed over a range of wave heights, periods and directions. The power of the most energetic and less frequent sea states can easily be more than one order of magnitude greater than the values observed in typical conditions. From an engineering point of view, since the WECs effectively operate on specific ranges of wave heights and periods, the feasibility study for wave energy production should be carried out considering the most representative sea states in terms of energy production. Wave power associated to the less frequent and most energetic states cannot be taken into account since its exploitation requires over-dimensioned infrastructures and the use of WECs that probably are not able to perform well in less energetic sea states. Directional distribution of wave energy should also be considered for non-point absorber devices. In Figs. 11 and 12 is provided an overview of the spatial variability of the distribution of wave power among heights, periods and directions at selected locations along western Sardinia, north-western and southern Sicily. In the lower left panel of the figures, the scatter plot represents the distribution of yearly average energy in terms of Te and Hs, evaluated over the 10 years simulated period. Contribution to the total energy given by individual sea states are lumped together in 0.25 s intervals of Te and 0.25 m intervals of Hs. Wave power contributions of individual 3-h sea states obtained from the model output are calculated using Equation (11). Lines of constant power are drawn on the scatter plots to highlight wave power variability. On the upper and right panels of each scatter plot two histograms represent the distribution of average yearly wave energy over Te and Hs respectively. The intervals used in the histograms are twice the size of the intervals used in the scatter plot for better graphic

representation. In each histogram a line represents the cumulative percentage of total energy available in terms of Te and Hs. Markers are placed every 10th percentile on the cumulative line. In the upper right panel a rose diagram describes the directional distribution of average yearly energy over 30 wide direction bins. Each concentric circle represents 20% contribution to the total wave energy. The plots in Fig. 11 refer to points located along western Sardinia coast (see Fig. 9). Sea states with significant wave heights between 2 and 4 m and significant periods between 8 and 10 s appear to carry a considerable amount of the total energy, both around 40%. There are however some notable differences between the various locations. Points 1.a and 1.b, located in the northern section of the coastline, only 50 km away, have similar values of average power flux and its distributions among Te and Hs are also nearly the same, however the directional distribution appears to be quite different with dominant directions shifted almost 45 apart. In these two locations the amount of power provided by the most extreme sea states, with Hs above 4 m, is around 40% of the total, while in the remaining locations this contribution reduces to about 30%. This is observed for points 1.c and 1.d, which are far less energetic than point 1.a and 1.b, but also for points 1.e and 1.f which share the same power levels of the first two points. Similarly, points 1.c and 1.d have the same energy content but different directional distributions. The wave energy distribution along points located off the Sicily coast follows a different pattern as shown in Fig. 12. Here the most energetic contributions in terms of significant period, accounting to 50% of the total, are located at lower values of Te, in the range between 6 and 8 s. Likewise, the main contribution to total energy in terms of Hs, reaching 50% of the total, is found in a lower range of values between 1.5 and 3.5 m. The main differences among the plots shown in Fig. 12 are in the wave energy directional distribution which has a prevalent NWW component but appears more or less scattered depending on the location. Points 2.e and 2.f have similar energy contents but directional distribution is extremely different with a concentrated distribution at point 2.e and a more scattered one at point 2.f. Furthermore, wave energy at these two locations is distributed in a narrower Hs band, when compared to the other points, with almost 70% of the total energy in the range between 1 and 3.5 m. 3.3. Wave power variability As a final remark we observe that temporal distribution of wave energy also plays an important role in the site selection. In the Mediterranean the seasonal distribution of sea states follows a pattern where the winter and fall seasons are the most energetic and calmer sea states are normally observed during the rest of the year [23]. The seasonal variability of the wave power flux we calculated shows a similar trend. Fig. 13 shows the spatial distribution of seasonal average power flux in the Mediterranean for the entire simulation period. As expected, the winter months of December, January and February are the most productive followed by the autumn ones. Wave power spatial distribution follows approximately the pattern described for the yearly average. Some differences can be found in the Central Mediterranean which appears to be especially energetic during the winter season and calm during the summer. The seasonal average power exhibits

Fig. 11. Distribution of wave energy as a function of significant wave period and significant wave height at points located along the western coast of Sardinia (see Fig. 9 points 1.ae 1.f). The lower left panel of each figure shows the average yearly energy associated with sea states identified by Te and Hs couples. Dotted lines mark reference power levels. Upper panel shows the energy distribution as a function of Te only; right panel as a function of Hs only. Lines in the upper and right panels are the cumulative energy as a percentage of the total. Dots on the cumulative lines mark each 10th percentile. Rose plot in the upper right panel shows energy distribution over wave incoming direction.

L. Liberti et al. / Renewable Energy 50 (2013) 938e949

Fig. 12. Same as Fig. 11 but for points located along the north-western and southern coast of Sicily (see Fig. 10 points points 2.ae2.f).

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Fig. 13. Seasonal distribution of average power per unit crest in the Mediterranean. Averages are calculated for the entire ten years simulation. DJF stands for December, January, February; MAM for March, April, May; JJA for June, July, August; SON September, October, November.

a considerable range of variation. In the Western Mediterranean average power flux is above 20 kW/m in large areas during the winter season but does not exceed 9 kW/m during the summer months. The seasonal variation range is wider, in relative terms, in the slightly less productive regions of the Eastern and Central Mediterranean. While the overall profitability of a given site could be evaluated in terms of the average energy production, taking into account its distribution over wave heights, periods and directions, the interannual fluctuations define the variability of the expected yearly revenues. In order to estimate the magnitude of the inter-annual fluctuation we take into account the Coefficient of Variation COV which was proposed by [4] as a measure of wave power temporal variability. COV is defined as:

COV ¼

s m

(13)

where m and s are defined in our analysis as the average and the standard deviation of the yearly mean wave power flux respectively. COV measures the variability of the observations with respect to their average value. The COV of a constant series of values is 0 while a COV of 1 means that the standard deviation equals the average value. Fig. 14 shows the spatial distribution of COV around the Italian peninsula. Average inter-annual fluctuations of average power flux above 20% of the overall average, corresponding to COV > 0.2 are commonly observed around the Italian peninsula. It appears that the fluctuation range is higher in sheltered areas. The highest COV values are observed in the southern Tyrrhenian where it exceeds 0.4, in parts of the Ionian, and in the region between the mainland and Corsica where it reaches values above 0.3. Along the western coast of Sardinia and along the north-western and southern coast of Sicily the inter-annual fluctuations are milder with COV < 0.25. The results of the analysis confirm that western Sardinia and southern and western Sicily are the most promising areas along the Italian coast for wave energy production also in terms of inter-annual variability.

Table 9 Statistics of buoy and ECMWF WAM model significant wave height (Hs) comparison.

Fig. 14. Distribution of the coefficient of variation (COV) of the yearly average power fluxes for years 2001e2010 around Italy. Dashed grid lines are 200 km apart.

Buoy

Bias (m)

Rmse (m)

Slope

si

d

Alghero Ancona Catania Crotone La Spezia Mazara del Vallo Ortona Ponza Monopoli Cetraro Capo Gallo

0.185 0.274 0.212 0.101 0.289 0.097 0.177 0.146 0.201 0.110 0.127

0.398 0.417 0.338 0.278 0.424 0.267 0.318 0.287 0.346 0.272 0.431

0.791 0.612 0.648 0.822 0.628 0.883 0.668 0.804 0.702 0.801 1.022

0.355 0.553 0.544 0.376 0.530 0.263 0.516 0.345 0.479 0.382 0.575

0.946 0.825 0.824 0.935 0.857 0.961 0.871 0.939 0.875 0.941 0.873

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4. Conclusions In this paper an in-depth analysis of the wave energy potential along the Italian coast was performed using a third generation ocean wave model. A wave climatology based on a ten years long simulation covering the period 2001e2010 was produced using the WAM Cycle 4.5.3 with a uniform resolution of 1/16 over the entire Mediterranean. Model results were validated against buoys and satellite altimeters data. We found that the spatial resolution of the wave model alone plays a major role in improving the overall quality of our results. To verify this finding, we compared wave data directly provided by the ECMWF at a 1/8 spatial resolution with buoy data for the years 2001e2010. The ECMWF wave data are produced by forcing the WAM model with ECMWF wind fields. Table 9 shows the values of the statistics obtained by comparing ECMWF Hs data with corresponding buoy measurements. By comparing the values of bias and slope found in Tables 5 and 9 we observe that the amount of Hs underestimation is considerably less in our simulations. Also the dispersion of model values is reduced as shown by the lower values of rmse and si. Starting from the model output a detailed analysis of wave energy availability and of its distribution among different sea states was carried out. As already stated by [9], among others, the most promising locations for wave energy production along the Italian coastline are found on the western coast of Sardinia and along the north-western and southern coast of Sicily. However, at the resolution of our model, we were able to observe that average power flux generally exhibits a considerable variability at relatively small spatial scales of the order of the tens of kilometers. For instance, along the western coast of Sardinia two areas located in the northern and southern sections of the coastline appear to be the most productive. Similarly, along the Sicily coastline the western section and the most southern tip are the most promising while the rest of the southern coast is far less energetic. This fact suggests that high resolution atlases of wave energy, like the one presented in this paper, are required for an accurate evaluation of most suitable locations for wave energy extraction. Moreover, by analyzing the wave energy distribution among sea states, we found that additional elements of variability emerge even between locations apparently homogeneous in terms of the overall energy potential. Since the actual amount of energy that can be extracted with a specific WEC depends on the distribution of available energy among sea states, it appears that a detailed knowledge of this information is extremely important to obtain reliable economical and technical assessments of wave energy parks. Acknowledgments The altimeter products were produced by CLS Space Oceanography Division and distributed by AVISO, with support from CNES. We are grateful to the CRESCO supercomputing facilities located at ENEA (http://www.cresco.enea.it). References [1] Association, EOE. Oceans of energy European ocean energy roadmap 20102050. Tech. Rep. European Ocean Energy Association; 2010. [2] Falcão AFDO. Wave energy utilization: a review of the technologies. Renewable and Sustainable Energy Reviews 2010;14(3):899e918. [3] IEA. Implementing agreement on ocean energy systems. Tech. Rep. IEA; 2010. [4] Cornett AM. A global wave energy resource assessment. In: proceedings of the Eighteenth international offshore and polar engineering conference, Vancouver, BC, Canada, 2008.

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